This repository showcases a collection of my practical deep learning projects, developed as part of my self-driven journey into the field of machine learning and artificial intelligence. With a background in accounting/audit/finance and a passion for data science, I transitioned into AI by learning through online resources, courses, and hands-on projects.
This repo is like a treasure chest of my learning adventures — from deep diving into reinforcement learning 🏊♀️ to playing with generative models 🤖. While I started as an accounting nerd, I soon realized the real magic happens when you let AI run wild. Every project here is a fun experiment, a challenge, or simply me trying to teach AI how to recognize cats 🐱 and generate cool images 🎨.
Just trying to become an AI wizard, one project at a time 🧙♂️. Some of my projects are experiments that worked, some… well, not so much (we all have our fair share of “oops” moments, right? 😅). But the real goal here is to build a strong portfolio for future AI jobs, so if you’re an employer checking this out… I promise it’s been fun, and I’m ready for more challenges!
Transitioning from a traditional industry (Audit, Finance) to AI isn’t easy, and that’s exactly why I want to share my learning roadmap with you! 🚀
If you’re curious or want to chat, feel free to drop a comment! Let’s connect and learn together! 🤝
• Python Programming: Mastered the fundamentals of Python, which is the cornerstone of my journey into data science and AI. 🐍
• Mathematics for Machine Learning: Completed essential mathematics courses covering linear algebra, calculus, and probability to understand the theory behind algorithms. 📐
• Introductory Machine Learning: Gained initial experience with basic supervised learning algorithms like linear regression and classification techniques. 📊
• Deep Learning Basics: Started exploring deep learning, neural networks, and backpropagation with Andrew Ng’s Deep Learning Specialization. 🧠
• Data Science & AI Specialization: Focused on building expertise in data preprocessing, feature engineering, and model training. I also got hands-on with various data analysis tools. 🔧
• Python for Data Science: Earned certifications such as PCEP (Python Entry-level) and PCAP (Python Associate) to solidify my Python expertise. 💻
• Machine Learning for Trading: Delved into machine learning applications for financial data and predictive modeling. 💵
• TensorFlow & PyTorch: Gained practical skills in TensorFlow and PyTorch, working on projects ranging from image classification to building neural networks for more advanced AI tasks. 📈
• MLOps: Explored MLOps principles to understand how to deploy machine learning models in production environments. ⚙️
• Reinforcement Learning: Started learning how machines can make decisions through reinforcement learning, a key area for building intelligent systems. 🎮
• AI for Medicine: Applied AI concepts in healthcare by working on projects in AI-powered diagnostics and medical data analysis. 🏥
• Generative Models: Tackled advanced topics like GANs, Diffusion Models, and Deep Generative Models to create realistic images, videos, and data. 🎨
• Natural Language Processing (NLP): Studied advanced techniques in NLP with a focus on transformers, BERT, and GPT models for tasks like text generation and language understanding. 📝
• AI Infrastructure: Explored AI infrastructure, focusing on scalable machine learning models and cloud-based solutions for AI applications. ☁️
• Advanced Generative Models: Specialized further in Stable Diffusion, Transformers for generative tasks, and cutting-edge AI techniques. ⚡
• Real-World Problem Solving: Engaged in complex projects such as building AI systems for specific industries, from healthcare to finance, tackling real-world challenges. 💡
• Learning to Learn: Continuously refined my critical thinking, problem-solving, and meta-learning skills, preparing for the next big breakthroughs in AI. 🧩
• AI Ethics & Responsible AI: Focused on understanding and integrating ethical considerations in AI development. 🌍
• Exploration of Emerging Technologies: Constantly exploring new AI techniques, tools, and frameworks to stay ahead in the fast-evolving AI field. 🚀
This repository documents my AI and deep learning journey, capturing my experimentation, learning, and project development across a wide array of topics. Each project here represents my ongoing efforts to apply theoretical concepts to real-world problems and showcase what I’ve learned so far.